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    Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines

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    Publication date
    2022-08
    End of Embargo
    2023-08-18
    Author
    Kabir, Sohag
    Aslansefat, K.
    Gope, P.
    Campean, I. Felician
    Papadopoulos, Y.
    Keyword
    Bayesian network
    Machine learning
    Offshore wind industry
    Reliability
    UAV
    Rights
    (c) 2022 IEEE. Full-text reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    embargoedAccess
    
    Metadata
    Show full item record
    Abstract
    The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.
    URI
    http://hdl.handle.net/10454/19141
    Version
    Accepted manuscript
    Citation
    Kabir S, Aslansefat K. Gope P et al (2022) Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines. IEEE International Conference on Computing, Electronics & Communications Engineering. 17-18 Aug 2022. Southend, UK.
    Link to publisher’s version
    https://doi.org/10.1109/iCCECE55162.2022.9875095
    Type
    Conference paper
    Notes
    The full-text of this paper will be released for public view at the end of the publisher embargo on 18 Aug 2023.
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    Engineering and Informatics Publications

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